Three years of wastewater surveillance for new psychoactive substances from 16 countries

The proliferation of new psychoactive substances (NPS) over recent years has made their surveillance complex. The analysis of raw municipal influent wastewater can allow a broader insight into community consumption patterns of NPS. This study examines data from an international wastewater surveillance program that collected and analysed influent wastewater samples from up to 47 sites in 16 countries between 2019 and 2022. Influent wastewater samples were collected over the New Year period and analysed using validated liquid chromatography - mass spectrometry methods. Over the three years, a total of 18 NPS were found in at least one site. Synthetic cathinones were the most found class followed by phenethylamines and designer benzodiazepines. Furthermore, two ketamine analogues, one plant based NPS (mitragynine) and methiopropamine were also quantified across the three years. This work demonstrates that NPS are used across different continents and countries with the use of some more evident in particular regions. For example, mitragynine has highest mass loads in sites in the United States, while eutylone and 3-methylmethcathinone increased considerably in New Zealand and in several European countries, respectively. Moreover, 2F-deschloroketamine, an analogue of ketamine, has emerged more recently and could be quantified in several sites, including one in China, where it is considered as one of the drugs of most concern. Finally, some NPS were detected in specific regions during the initial sampling campaigns and spread to additional sites by the third campaign. Hence, wastewater surveillance can provide an insight into temporal and spatial trends of NPS use

Luminous Thermal Flares from Quiescent Supermassive Black Holes

A dormant supermassive black hole lurking in the center of a galaxy will be revealed when a star passes close enough to be torn apart by tidal forces, and a flare of electromagnetic radiation is emitted when the bound fraction of the stellar debris falls back onto the black hole and is accreted. Here we present the third candidate tidal disruption event discovered in the GALEX Deep Imaging Survey: a 1.6x10^{43} erg s^{-1} UV/optical flare from a star-forming galaxy at z=0.1855. The UV/optical SED during the peak of the flare measured by GALEX and Palomar LFC imaging can be modeled as a single temperature blackbody with T_{bb}=1.7x10^{5} K and a bolometric luminosity of 3x10^{45} erg s^{-1}, assuming an internal extinction with E(B-V)_{gas}=0.3. The Chandra upper limit on the X-ray luminosity during the peak of the flare, L_{X}(2-10 keV)< 10^{41} erg s^{-1}, is 2 orders of magnitude fainter than expected from the ratios of UV to X-ray flux density observed in active galaxies. We compare the light curves and broadband properties of all three tidal disruption candidates discovered by GALEX, and find that (1) the light curves are well fitted by the power-law decline expected for the fallback of debris from a tidally disrupted solar-type star, and (2) the UV/optical SEDs can be attributed to thermal emission from an envelope of debris located at roughly 10 times the tidal disruption radius of a ~10^{7} M_sun central black hole. We use the observed peak absolute optical magnitudes of the flares (-17.5 > M_{g} > -18.9) to predict the detection capabilities of upcoming optical synoptic surveys. (Abridged)Comment: Accepted for Publication in ApJ, 19 pages, 10 figures, 2 tables, emulateapj, corrections from proofs adde

The NORMAN Suspect List Exchange (NORMAN-SLE): facilitating European and worldwide collaboration on suspect screening in high resolution mass spectrometry

Background: The NORMAN Association (https://www.norman-.network.com/) initiated the NORMAN Suspect List Exchange (NORMAN-SLE; https://www.norman-.network.com/nds/SLE/) in 2015, following the NORMAN collaborative trial on non-target screening of environmental water samples by mass spectrometry. Since then, this exchange of information on chemicals that are expected to occur in the environment, along with the accompanying expert knowledge and references, has become a valuable knowledge base for "suspect screening" lists. The NORMAN-SLE now serves as a FAIR (Findable, Accessible, Interoperable, Reusable) chemical information resource worldwide.Results: The NORMAN-SLE contains 99 separate suspect list collections (as of May 2022) from over 70 contributors around the world, totalling over 100,000 unique substances. The substance classes include per- and polyfluoroalkyl substances (PFAS), pharmaceuticals, pesticides, natural toxins, high production volume substances covered under the European REACH regulation (EC: 1272/2008), priority contaminants of emerging concern (CECs) and regulatory lists from NORMAN partners. Several lists focus on transformation products (TPs) and complex features detected in the environment with various levels of provenance and structural information. Each list is available for separate download. The merged, curated collection is also available as the NORMAN Substance Database (NORMAN SusDat). Both the NORMAN-SLE and NORMAN SusDat are integrated within the NORMAN Database System (NDS). The individual NORMAN-SLE lists receive digital object identifiers (DOIs) and traceable versioning via a Zenodo community (https:// zenodo.org/communities/norman-.sle), with a total of > 40,000 unique views, > 50,000 unique downloads and 40 citations (May 2022). NORMAN-SLE content is progressively integrated into large open chemical databases such as PubChem (https://pubchem.ncbi.nlm.nih.gov/) and the US EPA's CompTox Chemicals Dashboard (https://comptox. epa.gov/dashboard/), enabling further access to these lists, along with the additional functionality and calculated properties these resources offer. PubChem has also integrated significant annotation content from the NORMAN-SLE, including a classification browser (https://pubchem.ncbi.nlm.nih.gov/classification/#hid=101).Conclusions: The NORMAN-SLE offers a specialized service for hosting suspect screening lists of relevance for the environmental community in an open, FAIR manner that allows integration with other major chemical resources. These efforts foster the exchange of information between scientists and regulators, supporting the paradigm shift to the "one substance, one assessment" approach. New submissions are welcome via the contacts provided on the NORMAN-SLE website (https://www.norman-.network.com/nds/SLE/)

The NORMAN Suspect List Exchange (NORMAN-SLE): Facilitating European and worldwide collaboration on suspect screening in high resolution mass spectrometry

Background: The NORMAN Association (https://www.norman-network.com/) initiated the NORMAN Suspect List Exchange (NORMAN-SLE; https://www.norman-network.com/nds/SLE/) in 2015, following the NORMAN collaborative trial on non-target screening of environmental water samples by mass spectrometry. Since then, this exchange of information on chemicals that are expected to occur in the environment, along with the accompanying expert knowledge and references, has become a valuable knowledge base for “suspect screening” lists. The NORMAN-SLE now serves as a FAIR (Findable, Accessible, Interoperable, Reusable) chemical information resource worldwide. Results: The NORMAN-SLE contains 99 separate suspect list collections (as of May 2022) from over 70 contributors around the world, totalling over 100,000 unique substances. The substance classes include per- and polyfluoroalkyl substances (PFAS), pharmaceuticals, pesticides, natural toxins, high production volume substances covered under the European REACH regulation (EC: 1272/2008), priority contaminants of emerging concern (CECs) and regulatory lists from NORMAN partners. Several lists focus on transformation products (TPs) and complex features detected in the environment with various levels of provenance and structural information. Each list is available for separate download. The merged, curated collection is also available as the NORMAN Substance Database (NORMAN SusDat). Both the NORMAN-SLE and NORMAN SusDat are integrated within the NORMAN Database System (NDS). The individual NORMAN-SLE lists receive digital object identifiers (DOIs) and traceable versioning via a Zenodo community (https://zenodo.org/communities/norman-sle), with a total of > 40,000 unique views, > 50,000 unique downloads and 40 citations (May 2022). NORMAN-SLE content is progressively integrated into large open chemical databases such as PubChem (https://pubchem.ncbi.nlm.nih.gov/) and the US EPA’s CompTox Chemicals Dashboard (https://comptox.epa.gov/dashboard/), enabling further access to these lists, along with the additional functionality and calculated properties these resources offer. PubChem has also integrated significant annotation content from the NORMAN-SLE, including a classification browser (https://pubchem.ncbi.nlm.nih.gov/classification/#hid=101). Conclusions: The NORMAN-SLE offers a specialized service for hosting suspect screening lists of relevance for the environmental community in an open, FAIR manner that allows integration with other major chemical resources. These efforts foster the exchange of information between scientists and regulators, supporting the paradigm shift to the “one substance, one assessment” approach. New submissions are welcome via the contacts provided on the NORMAN-SLE website (https://www.norman-network.com/nds/SLE/)

The NORMAN Suspect List Exchange (NORMAN-SLE): facilitating European and worldwide collaboration on suspect screening in high resolution mass spectrometry

The NORMAN Association (https://www.norman-network.com/) initiated the NORMAN Suspect List Exchange (NORMAN-SLE; https://www.norman-network.com/nds/SLE/) in 2015, following the NORMAN collaborative trial on non-target screening of environmental water samples by mass spectrometry. Since then, this exchange of information on chemicals that are expected to occur in the environment, along with the accompanying expert knowledge and references, has become a valuable knowledge base for "suspect screening" lists. The NORMAN-SLE now serves as a FAIR (Findable, Accessible, Interoperable, Reusable) chemical information resource worldwide.The NORMAN-SLE project has received funding from the NORMAN Association via its joint proposal of activities. HMT and ELS are supported by the Luxembourg National Research Fund (FNR) for project A18/BM/12341006. ELS, PC, SEH, HPHA, ZW acknowledge funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 101036756, project ZeroPM: Zero pollution of persistent, mobile substances. The work of EEB, TC, QL, BAS, PAT, and JZ was supported by the National Center for Biotechnology Information of the National Library of Medicine (NLM), National Institutes of Health (NIH). JOB is the recipient of an NHMRC Emerging Leadership Fellowship (EL1 2009209). KVT and JOB acknowledge the support of the Australian Research Council (DP190102476). The Queensland Alliance for Environmental Health Sciences, The University of Queensland, gratefully acknowledges the financial support of the Queensland Department of Health. NR is supported by a Miguel Servet contract (CP19/00060) from the Instituto de Salud Carlos III, co-financed by the European Union through Fondo Europeo de Desarrollo Regional (FEDER). MM and TR gratefully acknowledge financial support by the German Ministry for Education and Research (BMBF, Bonn) through the project “Persistente mobile organische Chemikalien in der aquatischen Umwelt (PROTECT)” (FKz: 02WRS1495 A/B/E). LiB acknowledges funding through a Research Foundation Flanders (FWO) fellowship (11G1821N). JAP and JMcL acknowledge financial support from the NIH for CCSCompendium (S50 CCSCOMPEND) via grants NIH NIGMS R01GM092218 and NIH NCI 1R03CA222452-01, as well as the Vanderbilt Chemical Biology Interface training program (5T32GM065086-16), plus use of resources of the Center for Innovative Technology (CIT) at Vanderbilt University. TJ was (partly) supported by the Dutch Research Council (NWO), project number 15747. UFZ (TS, MaK, WB) received funding from SOLUTIONS project (European Union’s Seventh Framework Programme for research, technological development and demonstration under Grant Agreement No. 603437). TS, MaK, WB, JPA, RCHV, JJV, JeM and MHL acknowledge HBM4EU (European Union’s Horizon 2020 research and innovation programme under the grant agreement no. 733032). TS acknowledges funding from NFDI4Chem—Chemistry Consortium in the NFDI (supported by the DFG under project number 441958208). TS, MaK, WB and EMLJ acknowledge NaToxAq (European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie Grant Agreement No. 722493). S36 and S63 (HPHA, SEH, MN, IS) were funded by the German Federal Ministry for the Environment, Nature Conservation and Nuclear Safety (BMU) Project No. (FKZ) 3716 67 416 0, updates to S36 (HPHA, SEH, MN, IS) by the German Federal Ministry for the Environment, Nature Conservation, Nuclear Safety and Consumer Protection (BMUV) Project No. (FKZ) 3719 65 408 0. MiK acknowledges financial support from the EU Cohesion Funds within the project Monitoring and assessment of water body status (No. 310011A366 Phase III). The work related to S60 and S82 was funded by the Swiss Federal Office for the Environment (FOEN), KK and JH acknowledge the input of Kathrin Fenner’s group (Eawag) in compiling transformation products from European pesticides registration dossiers. DSW and YDF were supported by the Canadian Institutes of Health Research and Genome Canada. The work related to S49, S48 and S77 was funded by the MAVA foundation; for S77 also the Valery Foundation (KG, JaM, BG). DML acknowledges National Science Foundation Grant RUI-1306074. YL acknowledges the National Natural Science Foundation of China (Grant No. 22193051 and 21906177), and the Chinese Postdoctoral Science Foundation (Grant No. 2019M650863). WLC acknowledges research project 108C002871 supported by the Environmental Protection Administration, Executive Yuan, R.O.C. Taiwan (Taiwan EPA). JG acknowledges funding from the Swiss Federal Office for the Environment. AJW was funded by the U.S. Environmental Protection Agency. LuB, AC and FH acknowledge the financial support of the Generalitat Valenciana (Research Group of Excellence, Prometeo 2019/040). KN (S89) acknowledges the PhD fellowship through Marie Skłodowska-Curie grant agreement No. 859891 (MSCA-ETN). Exposome-Explorer (S34) was funded by the European Commission projects EXPOsOMICS FP7-KBBE-2012 [308610]; NutriTech FP7-KBBE-2011-5 [289511]; Joint Programming Initiative FOODBALL 2014–17. CP acknowledges grant RYC2020-028901-I funded by MCIN/AEI/1.0.13039/501100011033 and “ESF investing in your future”, and August T Larsson Guest Researcher Programme from the Swedish University of Agricultural Sciences. The work of ML, MaSe, SG, TL and WS creating and filling the STOFF-IDENT database (S2) mostly sponsored by the German Federal Ministry of Education and Research within the RiSKWa program (funding codes 02WRS1273 and 02WRS1354). XT acknowledges The National Food Institute, Technical University of Denmark. MaSch acknowledges funding by the RECETOX research infrastructure (the Czech Ministry of Education, Youth and Sports, LM2018121), the CETOCOEN PLUS project (CZ.02.1.01/0.0/0.0/15_003/0000469), and the CETOCOEN EXCELLENCE Teaming 2 project supported by the Czech ministry of Education, Youth and Sports (No CZ.02.1.01/0.0/0.0/17_043/0009632).Peer reviewe

Lake metabolism and the diel oxygen technique:state of the science

Significant improvements have been made in estimating gross primary production (GPP), ecosystem respiration (R), and net ecosystem production (NEP) from diel, “free-water” changes in dissolved oxygen (DO). Here we evaluate some of the assumptions and uncertainties that are still embedded in the technique and provide guidelines on how to estimate reliable metabolic rates from high-frequency sonde data. True whole-system estimates are often not obtained because measurements reflect an unknown zone of influence which varies over space and time. A minimum logging frequency of 30 min was sufficient to capture metabolism at the daily time scale. Higher sampling frequencies capture additional pattern in the DO data, primarily related to physical mixing. Causes behind the often large daily variability are discussed and evaluated for an oligotrophic and a eutrophic lake. Despite a 3-fold higher day-to-day variability in absolute GPP rates in the eutrophic lake, both lakes required at least 3 sonde days per week for GPP estimates to be within 20% of the weekly average. A sensitivity analysis evaluated uncertainties associated with DO measurements, piston velocity (k), and the assumption that daytime R equals nighttime R. In low productivity lakes, uncertainty in DO measurements and piston velocity strongly impacts R but has no effect on GPP or NEP. Lack of accounting for higher R during the day underestimates R and GPP but has no effect on NEP. We finally provide suggestions for future research to improve the technique

Genotype by environment interactions of harvest growth traits for barramundi (Lates calcarifer) commercially farmed in marine vs. freshwater conditions

Barramundi (Lates calcarifer), also known as Asian seabass, is a commercially important tropical aquaculture species farmed in diverse culture production systems and salinities (marine to freshwater). Despite adaptability to different culture conditions, selective breeding programs to improve growth rates in barramundi should consider the impact of genotype by environment (GxE) interactions on genetic gains. Barramundi juveniles from 144 families, originating from 24 dams and 54 sires were farmed in a seawater (SW) raceway in Bowen (QLD, Australia) and a freshwater (FW) pond environment in Townsville (QLD, Australia) - both operated under commercial culture conditions. Fish were sampled at 15 months post-hatch (mph) in the SW raceway (mean 1718 ± 309 g weight (W), 454 ± 28 mm total length (Lₜ) and 141 ± 11 mm body depth (BD) (n = 752)) and at 21 mph in the FW pond (mean 1905 ± 426 g W and 451 ± 39 mm Lt and 144 ± 15 mm BD (n = 752)). DNA parentage analyses were used to assign progeny to their respective parents, and the final dataset comprised of 1116 offspring. Moderate-low heritability estimates were found for body traits (W h² = 0.46 ± 0.10; Lt h² = 0.41 ± 0.12; BD h² = 0.49 ± 0.13; body shape H h² = 0.41 ± 0.12; and Fulton's K condition factor h² = 0.15 ± 0.07). Deformities (Def) were observed in 1.8% of fish in SW and 25.1% of fish in FW, although negligible additive genetic effects were evident (Def h² = 0.05 ± 0.04). GxE interactions were found to be moderate for harvest growth traits (W GxE rg = 0.81 ± 0.11; Lt GxE rg = 0.64 ± 0.18; BD GxE rg = 0.78 ± 0.13; H GxE rg = 0.71 ± 0.17), and high for Fulton's K condition factor (K GxE rg = 0.36 ± 0.31; P > 0.05). This study reveals the presence of weak to moderate re-ranking of genotypes for harvest growth traits in L. calcarifer farmed in marine and freshwater conditions, suggesting that GxE interactions should be taken into account in a breeding program servicing multiple environments. Incorporation of sib-information from extreme salinity environments into the selection criteria of a breeding program may therefore optimize the realization of genetic gains across distinct commercial conditions

Living Outside Germany: A Feasibility Study Concerning the Implementation of Interviews Abroad within the Framework of the German Socio-Economic Panel Study (SOEP) (Leben ausserhalb Deutschlands - Eine Machbarkeitsstudie zur Realisierung von Auslandsbefragungen auf Basis des Sozio-oekonomischen Panels (SOEP))

Mit der Pilotstudie "Leben außerhalb Deutschlands" beschreitet die Längsschnittstudie Sozio-oekonomisches Panel (SOEP) absolutes methodisches Neuland, indem versucht wird, die Adressen ausgewanderte Teilnehmer des deutsche Haushaltspanels SOEP im Ausland zu recherchieren und die Auswanderer mit Hilfe eines eigens entwickelten Fragebogens zu den Hintergründen ihres grenzüberschreitenden Umzugs schriftlich zu befragen. In den Jahren 2002 bis 2005 konnten 228 Auswanderer unter den SOEPTeilnehmern identifiziert werden. Nach erfolgreicher Adressrecherche war es möglich, an 52 Auswanderer den Fragebogen zu verschicken. Letztlich konnten auf diesem Weg 23 Befragungen realisiert werden. Geringe Selektivitätsprobleme sind hinsichtlich des Geschlechts, des Partnerschaftsstatus, der subjektiven Einschätzung des Gesundheitszustandes, der Lebenszufriedenheit und der Wohnregion vor der Auswanderung (alte vs. Neue Bundesländer) zu verzeichnen. Hingegen sind deutliche alters- und erwerbsstatusspezifische Selektivitätseffekte zu beobachten